</ol>
# imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
import json
import ipywidgets as widgets
import plotly
%matplotlib inline
# my favorite
plt.style.use("fivethirtyeight")
# show full columns
pd.set_option('display.max_columns', None)
# cell width
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:80% !important; }</style>"))
# listings data
ls = pd.read_csv("../data/listings.csv")
ls_d = pd.read_csv("../data/listings 2.csv")
# reviews data
rs = pd.read_csv("../data/reviews.csv")
rs_d = pd.read_csv("../data/reviews 2.csv")
# geography data
geo = pd.read_csv("../data/neighbourhoods.csv")
with open("../data/neighbourhoods.geojson") as jsonfile:
geojson = json.load(jsonfile)
import plotly.express as px
# set token
px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
fig = px.choropleth_mapbox(ls, geojson=geojson, color="neighbourhood", title="Washington D.C. Neighbourhood Map",
locations="neighbourhood", featureidkey="properties.neighbourhood",opacity=0.5, color_discrete_sequence=px.colors.qualitative.Light24,
center={"lat": 38.9072, "lon": -77.0369},
mapbox_style="light", zoom=11)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()